KEIO RESEARCHERS INFORMATION SYSTEM

Profile Summary 【 Display / hide 】

Profile Summary 【 Display / hide 】

• ・1999年04月-2001年03月 早稲田大学理工学部　応用物理学科助手 　橋本周司教授の研究室において、顔画像認識・合成、工業用精密画像計測、 　ヒューマノイドロボットの視覚システムに関する研究に従事． ・2002年04月-2005年03月 芝浦工業大学工学部情報工学科 専任講師（青木研究室発足） 　2005年04月-2008年3月 芝浦工業大学工学部情報工学科　准教授 　顔形状・動作の３次元画像解析技術の医学・歯学応用 　衛星画像他リモートセンシングデータの統合活用に関する研究 　道路交通画像システム，高精度画像計測システムに関する研究等に従事． 　※芝浦工業大学にて、７年間で約９０名の学生の研究指導を担当 ・2008年04月-現在　慶應義塾大学理工学部電子工学科　准教授 　人物を対象とした画像計測・認識技術、及び応用システムに関する研究． 　応用先として，セキュリティ，マーケティング，医療・福祉，美容，インターフェース，エンターテイメント，自動車，等を視野に入れ，幅広い産業応用を目指す． 　人の認知機構や感性を考慮したメディア理解技術とその応用，新しい視覚センサ，ロバスト画像特徴量に関する研究等に従事． ・2013年2月-現在　株式会社イデアクエスト　取締役兼任 　慶應理工発画像センシング技術の医療分野での実用化を目指している．

Career 【 Display / hide 】

Career 【 Display / hide 】

• 1999.04

  -

  2002.03

  早稲田大学, 理工学部　, 助手

• 2002.04

  -

  2005.03

  芝浦工業大学　, 工学部　情報工学科, 専任講師

• 2005.04

  -

  2008.03

  芝浦工業大学, 工学部　情報工学科, 助教授（2007より准教授）

• 2008.04

  -

  2017.03

  慶應義塾大学, 理工学部, 准教授

• 2013.02

  -

  2017.03

  株式会社イデアクエスト, 取締役

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Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

• 1996.03

  Waseda University, Faculty of Science and Engineering, 応用物理学科

  University, Graduated

• 1998.03

  Waseda University, Graduate School, Division of Science and Engineering, 物理学及応用物理学専攻

  Graduate School, Completed, Master's course

• 2001.02

  Waseda University, Graduate School, Division of Science and Engineering, 物理学及応用物理学専攻

  Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• 博士（工学）, Waseda University, Coursework, 2001.02

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Measurement engineering (Measurement Engineering)

• Informatics / Database (Media Informatics/Data Base)

• Informatics / Perceptual information processing (Perception Information Processing/Intelligent Robotics)

• Life Science / Medical systems (Medical Systems)

Books 【 Display / hide 】

Books 【 Display / hide 】

• Expert’s Gaze-Based Prediction Model for Assessing the Quality of Figure Skating Jumps

  Hirosawa S., Yamashita T., Aoki Y., Lecture Notes on Data Engineering and Communications Technologies, 2024

  　View Summary

  Researchers in computer vision are developing a method for Action Quality Assessment (AQA) that evaluates the quality of human actions in videos rather than identifying them. Specifically for figure skating, the task involves estimating the final scores from a video of a short program. It serves as an auxiliary assessment for judging skaters’ performances. Despite the significance of accurately predicting individual jump scores due to their substantial impact on final scores, prior studies have overlooked this aspect. Although videos concentrate on a solitary skater, they often include extraneous elements unrelated to assessing the quality. Consequently, expert humans discard non-essential data to make visually precise evaluations. Our research has illuminated the gaze patterns of judges and skaters when assessing jumps, developing a jump-performance prediction model that leverages their gaze patterns to filter out irrelevant information. In addition, we enhanced its predictive precision by incorporating kinematic data from a tracking system. The findings revealed a marked contrast in gaze patterns: skaters focused mainly on the face, while judges paid more attention to the lower body. Integrating these gaze patterns into our model improved its learning efficiency, with the model improved accuracy by assimilating the gaze data from both groups of specialists. Our work marks an innovative step towards merging human insight and artificial intelligence to tackle the challenge of jump performance evaluation in figure skating, offering valuable contributions to computer vision and sports science.

  • Access to Document (DOI)

• 画像センシングのしくみと開発がしっかりわかる教科書

  青木義満，輿水大和 他, 技術評論社, 2023.06,  Page： 239

• 顔の百科事典

  丸善出版, 2015.09

  Scope: 7 章 コンピュータと顔 ─顔の情報学─

  　View Summary

  顔を見ない日はないというくらい、「顔」は私達にとってあたり前の存在ですが、私達は一体どれほど「顔」のことを知っているのでしょうか。そのような「顔」を総合的に研究するのが「顔学」です。 顔学には、動物学や人類学をはじめ、解剖学、生理学、歯学、心理学、社会学の文化的な対象として扱われるだけでなく、演劇や美術などの芸術学、コンピュータの分野では、情報学、さらに、美容学、人相学など、実に多様な学問分野と関係しています。 本書では、私達と切り離すことのできない「顔」の、歴史的・文化的・社会的・科学的側面を中項目の事典としてまとめられていることにより、多様な分野を横断する知識にも容易にアクセスが可能になっています。 日本顔学会創立20周年記念出版として、「顔学」について体系化を行った、初めての百科事典です。

• 三次元画像センシングの新展開

  AOKI Yoshimitsu, NTS, 2015.05

  Scope: 第5章1節　色情報とレンジデータのフュージョンによる高分解能三次元レンジセンサの開発

• 電気学会125年史

  AOKI Yoshimitsu, 電気学会, 2013.05

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Papers 【 Display / hide 】

Papers 【 Display / hide 】

• Exploring the influence of judging experience on individual differences in figure skating jump performance evaluations: a case study of novice judges

  Hirosawa S., Kato T., Aoki Y.

  Sport Sciences for Health 22 ( 1 )  2026.03

  ISSN  18247490

  　View Summary

  Background: Judges in figure skating are tasked with evaluating the technical quality of skaters’ performances using the grade of execution (GOE) score, a process that requires rapid integration of multiple technical and qualitative visual cues under time constraints, despite the inherently subjective nature of the evaluation criteria. Previous research suggests that judging accuracy is more impacted by previous experience as an athlete. However, the influence of judging experience on individual differences in evaluation remains unclear. Aims: This study investigated whether novice judging experience reduces individual differences in figure skating jump evaluations. Methods: Three certified judges and three skaters with similar athletic abilities evaluated 30 double Axel jumps from the 2019 World Championships using video-based judging. Participants assigned GOE scores and evaluated six GOE criteria while eye movements were recorded, and individual differences were analyzed using (1) judging accuracy, (2) gaze coordinates, and (3) intraclass correlations coefficients. Results and Conclusions: Results indicated that novice judging experience was not associated with improved judging accuracy or reduced individual differences in final GOE scores. However, judging experience was associated with lower dispersion in gaze coordinates and higher intraclass correlations coefficients in GOE criterion-based evaluations. Notably, substantial individual variation persisted when evaluating jumps matched to music, suggesting a challenge for judge development programs.

  • Access to Document (DOI)

• Interaction Recognition Based on Relative Spatial Positions Between Individuals

  Suzuki M., Torimi K., Aoki Y.

  Seimitsu Kogaku Kaishi Journal of the Japan Society for Precision Engineering 92 ( 2 ) 193 - 198 2026

  ISSN  09120289

  　View Summary

  Interaction recognition between multiple individuals is crucial in many practical scenarios, particularly in sports analysis, yet remains challenging due to complex dynamics and directional ambiguity. Most existing methods primarily focus on single-person actions, which are insufficient for analyzing multi-person interactions. The SportsHHI dataset, designed specifically for human-human interactions in sports videos, provides detailed annotations but faces limitations from strict subject-object direction definitions, class imbalance, and inadequate encoding of relative spatial information. To overcome these issues, this study proposes three enhancements: (1) redefining relative spatial encoding to include global positional context, (2) introducing a directionality-agnostic evaluation method suitable for bidirectional interactions, and (3) employing focal loss to address class imbalance. Experimental results on SportsHHI demonstrate the effectiveness of the proposed improvements, achieving up to an increase 2.67 at mAP compared to baseline methods.

  • Access to Document (DOI)

• BasketLiDAR: The First LiDAR-Camera Multimodal Dataset for Professional Basketball MOT

  Hayashi R., Torimi K., Nagata R., Ikeda K., Sako O., Nakamura T., Tani M., Aoki Y., Yoshioka K.

  Mmsports 2025 8th International ACM Workshop on Multimedia Content Analysis in Sports    78 - 86 2025.10

  　View Summary

  Real-time 3D trajectory player tracking in sports plays a crucial role in tactical analysis, performance evaluation, and enhancing spectator experience. Traditional systems rely on multi-camera setups, but are constrained by the inherently two-dimensional nature of video data and the need for complex 3D reconstruction processing, making real-time analysis challenging. Basketball, in particular, represents one of the most difficult scenarios in the MOT field, as ten players move rapidly and complexly within a confined court space, with frequent occlusions caused by intense physical contact. To address these challenges, this paper constructs BasketLiDAR, the first multimodal dataset in the sports MOT field that combines LiDAR point clouds with synchronized multi-view camera footage in a professional basketball environment, and proposes a novel MOT framework that simultaneously achieves improved tracking accuracy and reduced computational cost. The BasketLiDAR dataset contains a total of 4,445 frames and 3,105 player IDs, with fully synchronized IDs between three LiDAR sensors and three multi-view cameras. We recorded 5-on-5 and 3-on-3 game data from actual professional basketball players, providing complete 3D positional information and ID annotations for each player. Based on this dataset, we developed a novel MOT algorithm that leverages LiDAR's high-precision 3D spatial information. The proposed method consists of a real-time tracking pipeline using LiDAR alone and a multimodal tracking pipeline that fuses LiDAR and camera data. Experimental results demonstrate that our approach achieves real-time operation, which was difficult with conventional camera-only methods, while achieving superior tracking performance even under occlusion conditions. The dataset is available upon request at: https://sites.google.com/keio.jp/keio-csg/projects/basket-lidar.

  • Access to Document (DOI)

• A Comprehensive Analysis of a Social Intelligence Dataset and Response Tendencies Between Large Language Models (LLMs) and Humans

  Mori E., Qiu Y., Kataoka H., Aoki Y.

  Sensors 25 ( 2 )  2025.01

  　View Summary

  In recent years, advancements in the interaction and collaboration between humans and have garnered significant attention. Social intelligence plays a crucial role in facilitating natural interactions and seamless communication between humans and Artificial Intelligence (AI). To assess AI’s ability to understand human interactions and the components necessary for such comprehension, datasets like Social-IQ have been developed. However, these datasets often rely on a simplistic question-and-answer format and lack justifications for the provided answers. Furthermore, existing methods typically produce direct answers by selecting from predefined choices without generating intermediate outputs, which hampers interpretability and reliability. To address these limitations, we conducted a comprehensive evaluation of AI methods on a video-based Question Answering (QA) benchmark focused on human interactions, leveraging additional annotations related to human responses. Our analysis highlights significant differences between human and AI response patterns and underscores critical shortcomings in current benchmarks. We anticipate that these findings will guide the creation of more advanced datasets and represent an important step toward achieving natural communication between humans and AI.

  • Access to Document (DOI)

• DynamicVLN: Incorporating Dynamics into Vision-and-Language Navigation Scenarios

  Sun Y., Qiu Y., Aoki Y.

  Sensors 25 ( 2 )  2025.01

  　View Summary

  Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent’s decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation. To bridge this gap, we propose a novel task called Dynamic Vision-and-Language Navigation (DynamicVLN), incorporating various dynamic scenarios to enhance the agent’s decision-making abilities and adaptability. By redefining the VLN task, we emphasize that a robust and generalizable agent should not rely solely on predefined instructions but must also demonstrate reasoning skills and adaptability to unforeseen events. Specifically, we have designed ten scenarios that simulate the challenges of dynamic navigation and developed a dedicated dataset of 11,261 instances using the CARLA simulator (ver.0.9.13) and large language model to provide realistic training conditions. Additionally, we introduce a baseline model that integrates advanced perception and decision-making modules, enabling effective navigation and interpretation of the complexities of dynamic road conditions. This model showcases the ability to follow natural language instructions while dynamically adapting to environmental cues. Our approach establishes a benchmark for developing agents capable of functioning in real-world, dynamic environments and extending beyond the limitations of static VLN tasks to more practical and versatile applications.

  • Access to Document (DOI)

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Papers, etc., Registered in KOARA 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

• Construction of feature extraction method for turbulence big data by machine learning

  Aoki, Yoshimitsu

  科学研究費補助金研究成果報告書 2020

• Gaze control and dexterous manipulation of infants' goal-directed actions : a comparison between 11 month olds and 18 month olds

  Aoki, Yoshimitsu

  Studies in sociology, psychology and education : inquiries into humans and societies (慶應義塾大学大学院社会学研究科)   ( 82 ) 17 - 35 2016

  ISSN  0912456X

• 産学連携のカタチ

  Aoki, Yoshimitsu

  新版 窮理図解 (慶應義塾大学理工学部)   ( 15 )  2014.01

• The way industry-and-academia collaboration is conducted

  Aoki, Yoshimitsu

  New Kyurizukai (Faculty of Science and Technology, Keio University)   ( 15 )  2014.01

Reviews, Commentaries, etc. 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

• 密集領域での動作を理解するためのハイブリッド型映像解析

  大内一成，小林大祐，中州俊信，青木義満

  東芝レビュー (東芝)  72 ( 4 ) 30 - 34 2017.09

  Internal/External technical report, pre-print, etc., Joint Work

• 画像センシング技術によるチームスポーツ映像からのプレー解析

  林　昌希，青木　義満

  映像情報メディア学会誌 (映像情報メディア学会)  70 ( 5 ) 710 - 714 2016.09

  Article, review, commentary, editorial, etc. (scientific journal), Joint Work

• Image Sensing Technologies and its Applications for Human Action Recognition

  AOKI Yoshimitsu

  Journal of JSNDI (日本非破壊検査協会)  65 ( 6 ) 254 - 260 2016.06

  Article, review, commentary, editorial, etc. (scientific journal), Single Work

• パターン計測技術の深化と広がる産業応用 -総論-

  AOKI Yoshimitsu

  計測と制御 (SICE)  53 ( 7 ) 555 - 556 2014.07

  Article, review, commentary, editorial, etc. (scientific journal), Single Work

Presentations 【 Display / hide 】

Presentations 【 Display / hide 】

• 自由な表現と被写体の質感を維持するメイク生成モデルの開発

  帯金駿, 田川晴菜, 中川雄介, 中村理恵, 青木義満

  [Domestic presentation]  第27回日本顔学会大会（フォーラム顔学2022）, 

  2022.09

  , 

  Oral presentation (general)

• 不確実性を考慮したセマンティックマップの生成

  竹中悠，森巧磨，谷口恭弘，青木義満

  [Domestic presentation]  第27回 知能メカトロニクスワークショップ, 

  2022.09

  , 

  Oral presentation (general)

• 重要パッチ選択に基づく効率的動画認識

  鈴木 智之, 青木 義満

  [Domestic presentation]  第25回 画像の認識・理解シンポジウム（MIRU2022）, 

  2022.07

  , 

  Poster presentation

• 音響信号を用いた人物の3次元姿勢推定

  川島穣, 柴田優斗, 五十川麻理子, 入江豪, 木村昭悟, 青木義満

  [Domestic presentation]  第25回 画像の認識・理解シンポジウム（MIRU2022）, 

  2022.07

  , 

  Oral presentation (general)

• 完全合成画像での学習による文書画像の影除去

  松尾祐飛，青木義満

  [Domestic presentation]  第28回画像センシングシンポジウム（SSII2022）, 

  2022.06

  , 

  Poster presentation

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Intellectual Property Rights, etc. 【 Display / hide 】

Intellectual Property Rights, etc. 【 Display / hide 】

• 画像処理装置，画像処理プログラムおよび画像処理方法

  Date applied： 2019-105297  2019.06 

  Joint

• 危険度推定装置，危険度推定方法及び危険度推定用コンピュータプログラム

  Date applied： 特願2015-005241  2015.01 

  Date issued： 特許第6418574号  2018.10

  Patent, Joint

Awards 【 Display / hide 】

Awards 【 Display / hide 】

• HCGシンポジウム2018 特集テーマセッション賞

  秋月 秀一(慶大)・大木 美加・バティスト ブロー・鈴木 健嗣(筑波大)・青木 義満(慶大), 2018.12, 電子情報通信学会ヒューマンコミュニケーショングループ, 床面プロジェクションに伴う動的な環境変化に対応する人物追跡技術

  Type of Award： Award from Japanese society, conference, symposium, etc.

• HCGシンポジウム2018 優秀インタラクティブ発表賞

  秋月 秀一(慶大)・大木 美加・バティスト ブロー・鈴木 健嗣(筑波大)・青木 義満(慶大), 2018.12, 電子情報通信学会ヒューマンコミュニケーショングループ, 床面プロジェクションに伴う動的な環境変化に対応する人物追跡技術

  Type of Award： Award from Japanese society, conference, symposium, etc.

• 精密工学会沼田記念論文賞

  加藤直樹，箱崎浩平，里雄二，古山純子，田靡雅基，青木ヨシミツ, 2018.03, 精密工学会, 畳み込みニューラルネットワークによる距離学習を用いた動画像人物再同定

  Type of Award： Award from Japanese society, conference, symposium, etc.

• IWAIT2018 Best Paper Award

  Ryunosuke Kurose, Masaki Hayashi, Yoshimitsu Aoki, 2018.01, IWAIT2018

  Type of Award： International academic award (Japan or overseas)

• IES-KCIC2017 Best Paper Award

  Siti Nor Khuzaimah Amit, Yoshimitsu Aoki, 2017.09, IEEE Indonesia Section, Disaster Detection from Aerial Imagery with Convolutional Neural Network

  Type of Award： International academic award (Japan or overseas)

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Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• ELECTRONICS AND ELECTRICAL ENGINEERING PRACTICAL RESEARCH ACTIVITIES B

  2026

• IMAGING SCIENCE AND TECHNOLOGY

  2026

• GRADUATE RESEARCH ON INTEGRATED DESIGN ENGINEERING 1

  2026

• ADVANCED IMAGING SCIENCE AND TECHNOLOGY

  2026

• GRADUATE RESEARCH ON ENGINEERING AND DESIGN 1

  2026

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Social Activities 【 Display / hide 】

Social Activities 【 Display / hide 】

• 画像情報教育振興協会

  2013.07

  -

  2015.03

• 独立行政法人 交通安全環境研究所

  2009.12

  -

  2012.03

Memberships in Academic Societies 【 Display / hide 】

Memberships in Academic Societies 【 Display / hide 】

• International Symposium on Optomechatronic Technologies 2013, 

  2013.04

  -

  2013.11

• International Workshop on Advanced Image Technology 2013(IWAIT2013), 

  2013.01

  -

  2013.09

• 11th International Conference on Quality Control by Artificial Vision(QCAV2013), 

  2012.12

  -

  2013.05

• 3rd International Conference on 3D Body Scanning Technologies, 

  2012.06

  -

  2012.10

• 計測自動制御学会パターン計測部会, 

  2012.04

  -

  Present

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Committee Experiences 【 Display / hide 】

Committee Experiences 【 Display / hide 】

• 2017.04

  -

  Present

  NEDO技術委員, NEDO

• 2016.07

  -

  2016.11

  Optics & Photonics Japan 2016　推進委員, 日本光学会

• 2016.07

  -

  2016.12

  Program committee member, International Workshop on Human Tracking and Behavior Analysis 2016

• 2015.09

  -

  2016.08

  第22回画像センシングシンポジウム　実行委員長, 画像センシング技術研究会

• 2014.09

  -

  2015.08

  第21回画像センシングシンポジウム　実行委員長, 画像センシング技術研究会

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